{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Simulators\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "This notebook shows how to import the *Qiskit Aer* simulator backend and use it to run ideal (noise free)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "wDHGd5xNLlRp"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Import Qiskit\n",
        "from qiskit import QuantumCircuit, transpile\n",
        "from qiskit_aer import AerSimulator\n",
        "from qiskit.visualization import plot_histogram, plot_state_city\n",
        "import qiskit.quantum_info as qi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "970GeWlQNsIq"
      },
      "source": [
        "## The AerSimulator\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtXOXfCXN41Z"
      },
      "source": [
        "\n",
        "1. `AerSimulator.available_devices()` : Return the available simulation devices.\n",
        "2. `AerSimulator.available_methods()` : Return the available simulation methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ViBewmJRM_Kq"
      },
      "outputs": [],
      "source": [
        "simulator = AerSimulator()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TveWKXEOPRXg"
      },
      "source": [
        "## Simulating a Quantum Circuit\n",
        "\n",
        "The basic operation runs a quantum circuit and returns a counts dictionary of measurement outcomes. Here we run a simple circuit that prepares a 2-qubit Bell-state $\\left|\\psi\\right\\rangle = \\frac{1}{\\sqrt{2}}\\left(\\left|0,0\\right\\rangle + \\left|1,1 \\right\\rangle\\right)$\n",
        "and measures both qubits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "FyfGD04tPOiZ",
        "outputId": "d6ec7079-116f-4651-cbb7-3920c5b5269c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create circuit\n",
        "circ = QuantumCircuit(2)\n",
        "circ.h(0)\n",
        "circ.cx(0, 1)\n",
        "circ.measure_all()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator()\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get counts\n",
        "result = simulator.run(circ).result()\n",
        "counts = result.get_counts(circ)\n",
        "plot_histogram(counts, title='Bell-State counts')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkRarb0ePkdz"
      },
      "source": [
        "\n",
        "### Returning measurement outcomes for each shot\n",
        "\n",
        "The `Simulator` also supports returning a list of measurement outcomes for each individual shot. This is enabled by setting the keyword argument `memory=True` in the run.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Npve6RQKPntJ",
        "outputId": "25716d56-bf08-4934-cc19-e26bd82ac3b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['11', '00', '11', '00', '00', '00', '00', '00', '11', '11']\n"
          ]
        }
      ],
      "source": [
        "# Run and get memory\n",
        "result = simulator.run(circ, shots=10, memory=True).result()\n",
        "memory = result.get_memory(circ)\n",
        "print(memory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEwPCts-Px8I"
      },
      "source": [
        "\n",
        "### Aer Simulator Options\n",
        "\n",
        "The `AerSimulator` backend supports a variety of configurable options which can be updated using the set_options method. See the AerSimulator API documentation for additional details.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Simulation Method\n",
        "\n",
        "The `AerSimulator` supports a variety of simulation methods, each of which supports a different set of instructions. The method can be set manually using `simulator(method=value)` option, or a simulator backend with a preconfigured method can be obtained directly from the `AerSimulator` \n",
        "\n",
        "When simulating ideal circuits, changing the method between the exact simulation methods `stabilizer`, `statevector`, `density_matrix` and `matrix_product_state` should not change the simulation result (other than usual variations from sampling probabilities for measurement outcomes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Simulation Method Option\n",
        "\n",
        "The simulation method is set using the method kwarg. A list supported simulation methods can be returned using available_methods(), these are\n",
        "\n",
        "1.  `\"automatic\"`: Default simulation method. Select the simulation method automatically based on the circuit and noise model.\n",
        "\n",
        "2.  `\"statevector\"`: A dense statevector simulation that can sample measurement outcomes from ideal circuits with all measurements at end of the circuit. For noisy simulations each shot samples a randomly sampled noisy circuit from the noise model.\n",
        "    \n",
        "    \n",
        "3. `\"density_matrix\"`: A dense density matrix simulation that may sample measurement outcomes from noisy circuits with all measurements at end of the circuit.\n",
        "    \n",
        "4. `\"stabilizer\"`: An efficient Clifford stabilizer state simulator that can simulate noisy Clifford circuits if all errors in the noise model are also Clifford errors.\n",
        "    \n",
        "    \n",
        "5. `\"extended_stabilizer\"`: An approximate simulated for Clifford + T circuits based on a state decomposition into ranked-stabilizer state. The number of terms grows with the number of non-Clifford (T) gates.\n",
        "    \n",
        "    \n",
        "6. `\"matrix_product_state\"`: A tensor-network statevector simulator that uses a Matrix Product State (MPS) representation for the state. This can be done either with or without truncation of the MPS bond dimensions depending on the simulator options. The default behaviour is no truncation.\n",
        "    \n",
        "    \n",
        "7. `\"unitary\"`: A dense unitary matrix simulation of an ideal circuit. This simulates the unitary matrix of the circuit itself rather than the evolution of an initial quantum state. This method can only simulate gates, it does not support measurement, reset, or noise.\n",
        "    \n",
        "8.  `\"superop\"`: A dense superoperator matrix simulation of an ideal or noisy circuit. This simulates the superoperator matrix of the circuit itself rather than the evolution of an initial quantum state. This method can simulate ideal and noisy gates, and reset, but does not support measurement.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "L-Yf5GZhPo0p",
        "outputId": "c2e02960-1e87-4565-8b2d-c96a73cb4b90"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Increase shots to reduce sampling variance\n",
        "shots = 10000\n",
        "\n",
        "\n",
        "\n",
        "# Statevector simulation method\n",
        "sim_statevector = AerSimulator(method='statevector')\n",
        "job_statevector = sim_statevector.run(circ, shots=shots)\n",
        "counts_statevector = job_statevector.result().get_counts(0)\n",
        "\n",
        "# Stabilizer simulation method\n",
        "sim_stabilizer = AerSimulator(method='stabilizer')\n",
        "job_stabilizer = sim_stabilizer.run(circ, shots=shots)\n",
        "counts_stabilizer = job_stabilizer.result().get_counts(0)\n",
        "\n",
        "\n",
        "# Extended Stabilizer method\n",
        "sim_extstabilizer = AerSimulator(method='extended_stabilizer')\n",
        "job_extstabilizer = sim_extstabilizer.run(circ, shots=shots)\n",
        "counts_extstabilizer = job_extstabilizer.result().get_counts(0)\n",
        "\n",
        "# Density Matrix simulation method\n",
        "sim_density = AerSimulator(method='density_matrix')\n",
        "job_density = sim_density.run(circ, shots=shots)\n",
        "counts_density = job_density.result().get_counts(0)\n",
        "\n",
        "# Matrix Product State simulation method\n",
        "sim_mps = AerSimulator(method='matrix_product_state')\n",
        "job_mps = sim_mps.run(circ, shots=shots)\n",
        "counts_mps = job_mps.result().get_counts(0)\n",
        "\n",
        "\n",
        "plot_histogram([ counts_statevector,counts_stabilizer ,counts_extstabilizer, counts_density, counts_mps],\n",
        "               title='Counts for different simulation methods',\n",
        "               legend=[ 'statevector',\n",
        "                       'density_matrix','stabilizer','extended_stabilizer', 'matrix_product_state'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YkWLUQbSdyD"
      },
      "source": [
        "\n",
        "#### Automatic Simulation Method\n",
        "\n",
        "The default simulation method is automatic which will automatically select a one of the other simulation methods for each circuit based on the instructions in those circuits. A fixed simulation method can be specified by by adding the method name when getting the backend, or by setting the method option on the backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "BEJqR-O1Rkss",
        "outputId": "8780440f-d13f-4432-852c-efccb4436b1a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# automatic\n",
        "sim_automatic = AerSimulator(method='automatic')\n",
        "job_automatic = sim_automatic.run(circ, shots=shots)\n",
        "counts_automatic = job_automatic.result().get_counts(0)\n",
        "\n",
        "plot_histogram([counts_automatic], title='Counts for automatic simulation method',legend=[ 'automatic'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FitzkDKPS3yZ"
      },
      "source": [
        "### GPU Simulation\n",
        "The `statevector`, `density_matrix` and `unitary` simulators support running on a NVidia GPUs. For these methods the simulation device can also be manually set to CPU or GPU using `simulator = AerSimmulator(method='statevector',device='GPU')` backend option.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "diCG8_tPSxLr"
      },
      "outputs": [],
      "source": [
        "from qiskit_aer import AerError\n",
        "\n",
        "# Initialize a GPU backend\n",
        "# Note that the cloud instance for tutorials does not have a GPU\n",
        "# so this will raise an exception.\n",
        "try:\n",
        "    simulator_gpu = AerSimulator(method='statevector', device='GPU')\n",
        "\n",
        "except AerError as e:\n",
        "    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0dn64qdTi3m"
      },
      "source": [
        "## Installing GPU Support\n",
        "\n",
        "In order to install and run the GPU supported simulators on Linux, you need CUDA® 11.2 or newer previously installed. CUDA® itself would require a set of specific GPU drivers. Please follow CUDA® installation procedure in the NVIDIA® [web](https://www.nvidia.com/drivers).\n",
        "\n",
        "If you want to install our GPU supported simulators, you have to install this other package:\n",
        "\n",
        "```python\n",
        "pip install qiskit-aer-gpu\n",
        "```\n",
        "\n",
        "The package above is for CUDA&reg 12, so if your system has CUDA® 11 installed, install separate package:\n",
        "\n",
        "```python\n",
        "pip install qiskit-aer-gpu-cu11\n",
        "```\n",
        "\n",
        "This will overwrite your current `qiskit-aer` package installation giving you the same functionality found in the canonical `qiskit-aer` package, plus the ability to run the GPU supported simulators: `statevector`, `density matrix`, and `unitary`.\n",
        "\n",
        "Note: This package is only available on x86_64 Linux. For other platforms that have CUDA support, you will have to build from source. You can refer to the [contributing guide](https://github.com/Qiskit/qiskit-aer/blob/main/CONTRIBUTING.md#building-with-gpu-support) for instructions on doing this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "yQcWcPcjTZL7"
      },
      "outputs": [],
      "source": [
        "from qiskit_aer import AerError\n",
        "\n",
        "# Initialize a GPU backend\n",
        "# Note that the cloud instance for tutorials does not have a GPU\n",
        "# so this will raise an exception.\n",
        "try:\n",
        "    simulator_gpu = AerSimulator(method='tensor_network', device='GPU')\n",
        "\n",
        "except AerError as e:\n",
        "    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKTD_kxGVL_q"
      },
      "source": [
        "### Simulation Precision\n",
        "\n",
        "One of the available simulator options allows setting the float precision for the `statevector`, `density_matrix`, `unitary` and `superop` methods. This is done using the `set_precision=\"single\"` or `precision=\"double\"` (default) option:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9W07XP4ZTpqJ",
        "outputId": "e585ddd8-e594-4e1f-8e1b-7a463b458b8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'00': 505, '11': 519}\n"
          ]
        }
      ],
      "source": [
        "simulator = AerSimulator(method='statevector')\n",
        "simulator.set_options(precision='single')\n",
        "\n",
        "# Run and get counts\n",
        "result = simulator.run(circ).result()\n",
        "counts = result.get_counts(circ)\n",
        "print(counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QtlisMIMVg3J"
      },
      "source": [
        "Setting the simulation precision applies to both CPU and GPU simulation devices. Single precision will halve the required memory and may provide performance improvements on certain systems."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Custom Simulator Instructions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Saving the simulator state\n",
        "\n",
        "The state of the simulator can be saved in a variety of formats using custom simulator instructions.\n",
        "\n",
        "\n",
        "| Circuit method | Description |Supported Methods | \n",
        "|----------------|-------------|------------------|\n",
        "| `save_state` | Save the simulator state in the native format for the simulation method | All | \n",
        "| `save_statevector` | Save the simulator state as a statevector | `\"automatic\"`, `\"statevector\"`, `\"matrix_product_state\"`, `\"extended_stabilizer\"`|\n",
        "| `save_stabilizer` | Save the simulator state as a Clifford stabilizer | `\"automatic\"`, `\"stabilizer\"`| \n",
        "| `save_density_matrix` | Save the simulator state as a density matrix | `\"automatic\"`, `\"statevector\"`, `\"matrix_product_state\"`, `\"density_matrix\"` |\n",
        "| `save_matrix_product_state` | Save the simulator state as a a matrix product state tensor | `\"automatic\"`, `\"matrix_product_state\"`|\n",
        "| `save_unitary` | Save the simulator state as unitary matrix of the run circuit | `\"automatic\"`, `\"unitary\"`|\n",
        "| `save_superop` | Save the simulator state as superoperator matrix of the run circuit | `\"automatic\"`, `\"superop\"`|\n",
        "\n",
        "Note that these instructions are only supported by the Aer simulator and will result in an error if a circuit containing them is run on a non-simulator backend such as an IBM Quantum device."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvwkMWpdVjqg"
      },
      "source": [
        "### Saving the final statevector\n",
        "To save the final statevector of the simulation we can append the circuit with the `save_statevector` instruction. Note that this instruction should be applied before any measurements if we do not want to save the collapsed post-measurement state\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        },
        "id": "QIZ90hUAVeW5",
        "outputId": "44cc3f7c-2532-4ff3-e04e-e774c0fefe8f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Construct quantum circuit without measure\n",
        "circ = QuantumCircuit(2)\n",
        "circ.h(0)\n",
        "circ.cx(0, 1)\n",
        "circ.save_statevector()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method='statevector')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get statevector\n",
        "result = simulator.run(circ).result()\n",
        "statevector = result.get_statevector(circ)\n",
        "plot_state_city(statevector, title='Bell state')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9iR2p-PV0Qx"
      },
      "source": [
        "\n",
        "### Saving the circuit unitary\n",
        "\n",
        "To save the unitary matrix for a `QuantumCircuit` we can append the circuit with the `save_unitary` instruction. Note that this circuit cannot contain any measurements or resets since these instructions are not supported on for the `\"unitary\"` simulation method\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNzLjdLFVwM4",
        "outputId": "7c5c8ad5-1a99-450c-d360-70250c12f236"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Circuit unitary:\n",
            " [[ 0.70711+0.j  0.70711-0.j  0.     +0.j  0.     +0.j]\n",
            " [ 0.     +0.j  0.     +0.j  0.70711+0.j -0.70711+0.j]\n",
            " [ 0.     +0.j  0.     +0.j  0.70711+0.j  0.70711-0.j]\n",
            " [ 0.70711+0.j -0.70711+0.j  0.     +0.j  0.     +0.j]]\n"
          ]
        }
      ],
      "source": [
        "# Construct quantum circuit without measure\n",
        "circ = QuantumCircuit(2)\n",
        "circ.h(0)\n",
        "circ.cx(0, 1)\n",
        "circ.save_unitary()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method = 'unitary')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get unitary\n",
        "result = simulator.run(circ).result()\n",
        "unitary = result.get_unitary(circ)\n",
        "print(\"Circuit unitary:\\n\", np.asarray(unitary).round(5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ap3rpflWDRY"
      },
      "source": [
        "\n",
        "### Saving multiple states\n",
        "\n",
        "We can also apply save instructions at multiple locations in a circuit. Note that when doing this we must provide a unique label for each instruction to retrieve them from the results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSYho6W8WBkI",
        "outputId": "7871b3c9-6f33-4677-e9fb-70afe7bd0f00"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'psi_5': Statevector([-1.+0.00000000e+00j,  0.-5.55111512e-17j],\n",
              "             dims=(2,)),\n",
              " 'psi_4': Statevector([-0.30901699+0.j        ,  0.        -0.95105652j],\n",
              "             dims=(2,)),\n",
              " 'psi_3': Statevector([0.58778525+0.j        , 0.        -0.80901699j],\n",
              "             dims=(2,)),\n",
              " 'psi_2': Statevector([0.95105652+0.j        , 0.        -0.30901699j],\n",
              "             dims=(2,)),\n",
              " 'psi_1': Statevector([1.+0.j, 0.+0.j],\n",
              "             dims=(2,)),\n",
              " 'psi_0': Statevector([1.+0.j, 0.+0.j],\n",
              "             dims=(2,))}"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Construct quantum circuit without measure\n",
        "steps = 5\n",
        "circ = QuantumCircuit(1)\n",
        "for i in range(steps):\n",
        "    circ.save_statevector(label=f'psi_{i}')\n",
        "    circ.rx(i * np.pi / steps, 0)\n",
        "circ.save_statevector(label=f'psi_{steps}')\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method= 'automatic')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get saved data\n",
        "result = simulator.run(circ).result()\n",
        "data = result.data(0)\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCM2Zy0SWLq8"
      },
      "source": [
        "### Setting the simulator to a custom state\n",
        "\n",
        "The `AerSimulator` allows setting a custom simulator state for several of its simulation methods using custom simulator instructions\n",
        "\n",
        "| Circuit method | Description |Supported Methods | \n",
        "|----------------|-------------|------------------|\n",
        "| `set_statevector` | Set the simulator state to the specified statevector | `\"automatic\"`, `\"statevector\"`, `\"density_matrix\"`|\n",
        "| `set_stabilizer` | Set the simulator state to the specified Clifford stabilizer | `\"automatic\"`, `\"stabilizer\"`| \n",
        "| `set_density_matrix` | Set the simulator state to the specified  density matrix | `\"automatic\"`, `\"density_matrix\"` |\n",
        "| `set_unitary` | Set the simulator state to the specified  unitary matrix | `\"automatic\"`, `\"unitary\"`, `\"superop\"`|\n",
        "| `set_superop` | Set the simulator state to the specified superoperator matrix | `\"automatic\"`, `\"superop\"`|\n",
        "\n",
        "\n",
        "**Notes:**\n",
        "* These instructions must be applied to all qubits in a circuit, otherwise an exception will be raised.\n",
        "* The input state must also be a valid state (statevector, density matrix, unitary etc) otherwise an exception will be raised.\n",
        "* These instructions can be applied at any location in a circuit and will override the current state with the specified one. Any classical register values (e.g. from preceding measurements) will be unaffected\n",
        "* Set state instructions are only supported by the Aer simulator and will result in an error if a circuit containing them is run on a non-simulator backend such as an IBM Quantum device."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQ-0aFF8WQjV"
      },
      "source": [
        "#### Setting a Custom Statevector\n",
        "\n",
        "The set_statevector instruction can be used to set a custom Statevector state. The input statevector must be valid ($|\\langle\\psi|\\psi\\rangle|=1$)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPRaGfd2WJMZ",
        "outputId": "ca03e66c-0dd0-473d-d433-b2e554aa9722"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'statevector': Statevector([-0.49859823-0.41410205j,  0.12480824+0.46132192j,\n",
              "               0.33634191+0.30214216j,  0.234309  +0.3036574j ],\n",
              "             dims=(2, 2))}"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Generate a random statevector\n",
        "num_qubits = 2\n",
        "psi = qi.random_statevector(2 ** num_qubits, seed=100)\n",
        "\n",
        "# Set initial state to generated statevector\n",
        "circ = QuantumCircuit(num_qubits)\n",
        "circ.set_statevector(psi)\n",
        "circ.save_state()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method='statevector')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get saved data\n",
        "result = simulator.run(circ).result()\n",
        "result.data(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLB4ehiEWfU9"
      },
      "source": [
        "#### Using the initialize instruction\n",
        "\n",
        "It is also possible to initialize the simulator to a custom statevector using the `initialize` instruction. Unlike the `set_statevector` instruction this instruction is also supported on real device backends by unrolling to reset and standard gate instructions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDDJgfYEWcK6",
        "outputId": "527c50db-d0e1-4615-9ee4-e4bd9eda9df8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'statevector': Statevector([-0.49859823-0.41410205j,  0.12480824+0.46132192j,\n",
              "               0.33634191+0.30214216j,  0.234309  +0.3036574j ],\n",
              "             dims=(2, 2))}"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Use initilize instruction to set initial state\n",
        "circ = QuantumCircuit(num_qubits)\n",
        "circ.initialize(psi, range(num_qubits))\n",
        "circ.save_state()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method= 'statevector')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get result data\n",
        "result = simulator.run(circ).result()\n",
        "result.data(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFXx_joWWmct"
      },
      "source": [
        "#### Setting a custom density matrix\n",
        "\n",
        "The `set_density_matrix` instruction can be used to set a custom `DensityMatrix` state. The input density matrix must be valid ($Tr[\\rho]=1, \\rho \\ge 0$)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plZWFBv2WkoZ",
        "outputId": "1a557650-d87d-46eb-a00e-2342817aa156"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'density_matrix': DensityMatrix([[ 0.2075308 +0.j        ,  0.13161422-0.01760848j,\n",
              "                  0.0442826 +0.07742704j,  0.04852053-0.01303171j],\n",
              "                [ 0.13161422+0.01760848j,  0.20106116+0.j        ,\n",
              "                  0.02568549-0.03689812j,  0.0482903 -0.04367912j],\n",
              "                [ 0.0442826 -0.07742704j,  0.02568549+0.03689812j,\n",
              "                  0.39731492+0.j        , -0.01114025-0.13426423j],\n",
              "                [ 0.04852053+0.01303171j,  0.0482903 +0.04367912j,\n",
              "                 -0.01114025+0.13426423j,  0.19409312+0.j        ]],\n",
              "               dims=(2, 2))}"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "num_qubits = 2\n",
        "rho = qi.random_density_matrix(2 ** num_qubits, seed=100)\n",
        "circ = QuantumCircuit(num_qubits)\n",
        "circ.set_density_matrix(rho)\n",
        "circ.save_state()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method='density_matrix')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get saved data\n",
        "result = simulator.run(circ).result()\n",
        "result.data(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTr8TswdWuCZ"
      },
      "source": [
        "#### Setting a custom stabilizer state\n",
        "\n",
        "The `set_stabilizer` instruction can be used to set a custom `Clifford` stabilizer state. The input stabilizer must be a valid `Clifford`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVwlRcf6WsAb",
        "outputId": "c732a2e0-5cfd-4623-f9d9-648542df8380"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'stabilizer': StabilizerState(['+ZZ', '-IZ'])}"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Generate a random Clifford C\n",
        "num_qubits = 2\n",
        "stab = qi.random_clifford(num_qubits, seed=100)\n",
        "\n",
        "# Set initial state to stabilizer state C|0>\n",
        "circ = QuantumCircuit(num_qubits)\n",
        "circ.set_stabilizer(stab)\n",
        "circ.save_state()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method= \"stabilizer\")\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get saved data\n",
        "result = simulator.run(circ).result()\n",
        "result.data(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ex70kNx6W2Z7"
      },
      "source": [
        "#### Setting a custom unitary\n",
        "\n",
        "The `set_unitary` instruction can be used to set a custom unitary `Operator` state. The input unitary matrix must be valid ($U^\\dagger U=\\mathbb{1}$)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJuGWd9pW0HZ",
        "outputId": "bf26add3-bbdd-47bd-e60c-32a5472beb89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'unitary': Operator([[-0.44885724-0.26721573j,  0.10468034-0.00288681j,\n",
              "             0.4631425 +0.15474915j, -0.11151309-0.68210936j],\n",
              "           [-0.37279054-0.38484834j,  0.3820592 -0.49653433j,\n",
              "             0.14132327-0.17428515j,  0.19643043+0.48111423j],\n",
              "           [ 0.2889092 +0.58750499j,  0.39509694-0.22036424j,\n",
              "             0.49498355+0.2388685j ,  0.25404989-0.00995706j],\n",
              "           [ 0.01830684+0.10524311j,  0.62584001+0.01343146j,\n",
              "            -0.52174025-0.37003296j,  0.12232823-0.41548904j]],\n",
              "          input_dims=(2, 2), output_dims=(2, 2))}"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Generate a random unitary\n",
        "num_qubits = 2\n",
        "unitary = qi.random_unitary(2 ** num_qubits, seed=100)\n",
        "\n",
        "# Set initial state to unitary\n",
        "circ = QuantumCircuit(num_qubits)\n",
        "circ.set_unitary(unitary)\n",
        "circ.save_state()\n",
        "\n",
        "# Transpile for simulator\n",
        "simulator = AerSimulator(method='unitary')\n",
        "circ = transpile(circ, simulator)\n",
        "\n",
        "# Run and get saved data\n",
        "result = simulator.run(circ).result()\n",
        "result.data(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CImqrva-W9SQ",
        "outputId": "b23060be-8135-40ca-e70e-a82f2251512b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.0.1'"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import qiskit\n",
        "qiskit.__version__"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
